Apparatus, engine, system and method for predictive analytics in a manufacturing system

ABSTRACT

A predictive analytics apparatus, engine, system and method capable of providing real time analytics in a manufacturing system. The apparatus, engine, system and method may include a data input capable of receiving raw data output from at least one machine operable to effect the manufacturing system embodiments, and a processor associated with a computing memory and suitable for executing code from the computing memory. The code may include an adaptor capable of pushing the received raw data to one or more databases to processed data; an extractor capable of extracting the processed data from the one or more databases; predictive analytics capable of receiving the extracted processed data and applying thereto at least one predictive model including target data for the at least one machine, and capable of providing feedback to the at least one machine to modify performance of the at least one machine based on the application of the at least one predictive model; and a visualizer capable of providing at least a visualization of the feedback and of the performance.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure is a Continuation Application to U.S. applicationSer. No. 16/302,477, entitled: “APPARATUS, ENGINE, SYSTEM AND METHOD FORPREDICTIVE ANALYTICS IN A MANUFACTURING SYSTEM,” filed Nov. 16, 2018,which claims priority to PCT Application No. PCT/US2017/032962,entitled: “APPARATUS, ENGINE, SYSTEM AND METHOD FOR PREDICTIVE ANALYTICSIN A MANUFACTURING SYSTEM,” filed May 16, 2017, which claims priority toU.S. Provisional Application No. 62/337,006, entitled “APPARATUS,ENGINE, SYSTEM AND METHOD FOR PREDICTIVE ANALYTICS IN A MANUFACTURINGSYSTEM,” filed May 16, 2016, the contents of which is incorporated byreference in its entireties herein.

BACKGROUND Field of the Description

The present disclosure relates to analytics, and, more particularly, toan apparatus, engine, system and method for predictive analytics in amanufacturing system.

Description of the Background

In the present state of the art, manufacturing, particularly acrosslines and across facilities, employs any number of machines, dependentupon the product being manufactured. These machines, while generallycapable of providing operational data, often provide that data indisparate and/or unstructured formats that make the data unsuitable formonitoring of the machines during the manufacturing process.Consequently, it is typically only after a process-breakdown, alower-than-expected yield, or a functional decay of one or more machinesthat an issue in a line or in a facility is detected. That is, presentissues in manufacturing are generally only detected after or as theyoccur.

Therefore, the need exists for an apparatus, engine, system and methodof materials movement for predictive analytics in a manufacturingsystem.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the disclosed embodiments detailed herein. In thedrawings, like numerals represent like elements, and:

FIGS. 1-28 illustrate aspects of the embodiments.

DETAILED DESCRIPTION

The figures and descriptions provided herein may have been simplified toillustrate aspects that are relevant for a clear understanding of theherein described apparatuses, devices, systems, and methods, whileeliminating, for the purpose of brevity and clarity, other aspects thatmay be found in typical similar devices, systems, and methods. Those ofordinary skill may thus recognize that other elements and/or operationsmay be desirable and/or necessary to implement the devices, systems, andmethods described herein. But because such elements and operations areknown in the art or are evident from the discussion herein, and becausethey do not facilitate a better understanding of the present disclosure,a discussion of such elements and operations may not be provided herein.However, the present disclosure is deemed to include all such elements,variations, and modifications to the described aspects that would beknown to those of ordinary skill in the art in light of the discussionherein.

Exemplary embodiments are provided throughout so that this disclosure issufficiently thorough and fully conveys the scope of the disclosedembodiments to those who are skilled in the art. Numerous specificdetails are set forth, such as examples of specific components, devices,and methods, to provide this thorough understanding of embodiments ofthe present disclosure. Nevertheless, it will be apparent to thoseskilled in the art that specific disclosed details need not be employed,and that exemplary embodiments may accordingly be embodied in differentforms. As such, the exemplary embodiments should not be construed tolimit the scope of the disclosure. In some exemplary embodiments,well-known processes, well-known device structures, and well-knowntechnologies may not be described in detail.

The terminology used herein is for the purpose of describing particularexemplary embodiments only and is not intended to be limiting. Forexample, as used herein the singular forms “a”, “an” and “the” may beintended to include the plural forms as well, unless the context clearlyindicates otherwise. The terms “comprises,” “comprising,” “including,”and “having,” are inclusive and therefore specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. The steps, processes, and operations described herein are notto be construed as necessarily requiring their respective performance inthe particular order discussed or illustrated, unless specificallyidentified as a preferred order of performance. It is also to beunderstood that additional or alternative steps may be employed.

When an element or layer is referred to as being “on”, “engaged to”,“connected to”, “coupled to”, or like term in relation to anotherelement or layer, it may be directly on, engaged, connected or coupledto the other element or layer, or intervening elements or layers may bepresent. In contrast, when an element is referred to as being “directlyon,” “directly engaged to”, “directly connected to”, “directly coupledto”, or like term in relation to another element or layer, there may beno intervening elements or layers present. Other words used to describethe relationship between elements should be interpreted in a likefashion (e.g., “between” versus “directly between,” “adjacent” versus“directly adjacent,” etc.). As used herein, the term “and/or” includesany and all combinations of one or more of the associated listed items.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be used only to distinguishone element, component, region, layer or section from another element,component, region, layer or section. That is, terms such as “first,”“second,” and other numerical terms when used herein do not imply asequence or order unless clearly indicated by the context. Thus, a firstelement, component, region, layer or section discussed below could betermed a second element, component, region, layer or section withoutdeparting from the teachings of the exemplary embodiments.

The disclosed embodiments use realtime machine learning data, which maybe unstructured, with, for example, an adaptor, such as CAMx or ActiveX, in order to push realtime data to one or more databases, such as aSQL database and/or Mongo database, to allow for extraction to, forexample, the analytics layer. Extraction to the analytics layer mayallow for modeling, such as of supervised and/or unsupervisedoperations, which enables prediction ofperformance/failures/occurrences/triggers downstream. In short, rawmachine-data text logs may be mined for conversion to structured datafor analytics. Visualization of the modeling may allow for triggering ofactions and/or reactions, such as via flow charts and decision/alerttrees.

Accordingly, embodiments may include an approach that utilizes, forexample, four layers of an OSI-type model. The four layers may includethe device/machine unstructured data in a first layer; the structuringof that data in a second layer; analytics, such as includingmodeling/prediction, of the structured data in a third layer; andfinally visualization of the analytics, such as to drive action, in afourth layer. Such visualization may include, for example, reaction flowcharts, such as to mitigate the source of the failure.

This multi-layer software application may be structured as two maincomponents: a modeling framework and approach to build predictive modelsfor product quality; and an integrated platform that enables modelbuilds and execution in a global production environment. Thereby, theembodiments may provide predictive analytics that may impact, end toend, a supply chain. That is, embodiments may provide early warning ofcomplete supply chain failures to prevent outflow to customers, all theway back to serial data acquisition from machines and machine-to-machineinterfaces for traceability of early warning failures in a machine orline before they happen. Such predictive analytics may drive reductionof warranty claim cost, improve production yields, provide notificationof proliferation throughout the supply chain of potential failuresbefore they happen, increase production capacity, and improve componentsupplier quality levels to drive down accruals on warranty claims, amongmany other benefits.

Predictive analytics as applied to realtime machine data in order tomodel/predict failures before they occur downstream also improvesproduction yields, and reduces scrap, work in process, and excessinventory, while also reducing energy consumption, eliminating back endtests, and increasing production capacity to customers. The end to endnature of the embodiments allows for application starting from thecustomer design, then to component suppliers, manufacturing, customersystem and finally to customer warranty issues.

The defined data integration path provided by the embodiments allows forrapid analytics in production environment. These analytics may reducedowntime variably dependent upon the production environment in which theanalytics/modeling are employed, such as by 25-40% in some environments,or 2-3 times or more in others, such as by modifying process parametersor operations to reduce eventual failures or to enhance efficiencies.

The modeling may alert to prospective first time fails or repeatedfails, critical points or elements, and may thus provide improvedmachine availability, key uptime data points and early failureprediction. To allow for the modeling, various data manipulation mayoccur, such as: a data quality score card to identify poor structure ofmachine log format (data quality assurance allows for modeling to beenabled); a roll-up strategy, such as a 15 second roll-up, thattime-stitches data, such as to overcome data aggregation hurdle thatarises from data being collected at differing frequencies from differentmachines; a sequential dimension reduction strategy to identifyparameters that impact downtime; clean up of variables; removal ofvariables with erroneous data or large sets of missing values;calculation of information value of certain data sets; evaluation ofpredictor importance to a model; logistic regression models thatcalculate percentage of importance in predicting response for each inputvariable; empirical parameter evaluation and feedback; use of raw datato identify fail rate trends across parameter distribution for variablesor top priority variables identified by modeling. This type of data andparameter management, such as when focused in the lower failure region,may provide an opportunity to increase uptime significantly, such asbetween 10-30%. Increasing machine uptime may provide an opportunity tosignificantly increase production, such as by an entire day's worth ofproduction within a week's time period, by way of non-limiting example.

The qualitative analysis of data discussed throughout allows for theconnection of multiple machines/facilities, such as for big datacollection. This also enables a self-improving manufacturing ecosystem.Accordingly, local and/or master/remote analytics dashboards may providereal-time information and analytics for one or more local or remotemachines, lines and/or across one or more facilities.

FIG. 1 illustrates an exemplary four layer software solution. Includedare a device layer, which may employ, for example, CAMx, VVTS, or thelike; a data layer, which may employ Mongo dB, SQL Server, or the like;an analytics tools layer, which may employ MS Azure M-L, R, DiscoveryModule, or the like, and which may operate supervised or unsupervisedlearning module(s); and a visualization and reporting layer, which mayemploy Angular.js, MVC, SFM, or the like.

This software solution application may minimize yield loss and increaseproduction capacity by providing tools to assist in automating themanufacturing process, which would otherwise have to be performed ad-hocand by manual testing. By maximizing the operation of production line,the system may meet a site's business and operations requirements whileremaining flexible and easy to understand, such as by providing logicalUser Interface akin to the dashboards discussed above.

More specifically, the software application may allow quality or workcell managers to direct and communicate with a group of manufacturingengineers/operators and site staff, such as to prevent production linefailures and to publish system alerts on the shop floor and/or store ona data warehouse. The software application may facilitate near real-timeproduction data communication between suppliers, operators,manufacturing engineers, production, and operations via electroniccommunication methods. The user interface screens discussed herein maybe used in some or every stage of the development through the system toprovide a uniform review of triggers-process alarms; the location, lookand feel of these user interface screens may be configurable via theapplication's menu options and business segment requirements forproducts being produced. The software application system may alsocontain a relational database containing a list of operators,manufacturing engineers, operations, quality and component suppliers whomay be integrated into an alerts protocol process.

The software application solution enables production lines withanalytics capability that is fully integrated with a current plant shopfloor systems. This delivers an integrated quality analytics solutionthat provides real or near-real time prediction of quality. Thetimeliness of the data and analytics may be further enhanced using knownmeans, such as wherein the software application may avoid pullingreal-time data from production databases (MES, SFM) during predictiveanalytic data staging and aggregation to avoid high data transfer whichcould affect production data acquisition for operations.

The predictive analytics aspects of the software application may takeupstream process inspection and control parameters and build predictivemodels for quality metrics. These models are then later used inpredictive mode for new material that is being processed. Thispredictive ability predicts quality issues before they are detected intraditional control methods and enables benefits such as reduced scrap,reduced rework, managing inventory based on known propensity to qualityissues, and improved customer satisfaction, along with other benefits.

The predictive analytics aspects of the software application may enablethe discussed real-time machine data acquisition through variousadaptors that live stream to Test or Mongo databases which may extract,transfer, and load for aggregation/staging of data. This may enablemodel learning that will score events that predict and trigger alarms.These events may be driven by reaction flow actions driven by theoperators, test engineers, manufacturing engineers, work cell qualitymanagers and work cell operations managers. The reaction flow charts mayhave a series of escalation points that will enable decisions to be madeon production line stoppage, removal of products for failure analysis,notifications to suppliers in real-time of triggered excursions.

FIG. 2 is a block diagram illustrating an exemplary data flow andmanipulation in accordance with the exemplary layers indicated in FIG. 1. As illustrated, loaded data may be transformed and ultimatelyextracted for accessibility to enterprise level applications. FIG. 3further illustrates this interaction by indicating the loading of datafrom machine log files, whereby certain of the data is data based and/orotherwise manipulated prior to being extracted for availability to anultimate user, such as via a public or private network.

FIG. 4 illustrates an exemplary data flow through the data stackdiscussed with respect to FIG. 1 . In the illustration, the data isdatabased as generated by the machines and machine interfaces, isthereafter manipulated to allow for analytics, is thereafter subjectedto analytics in the form of analysis tools, and is thereafter visualizedfor generation of reporting and/or alerts. Of note, those skilled in theart will appreciate with respect to FIG. 4 that virtualization may notbe separately needed in the flow illustrated.

The software application may be designed to trigger real-time alerts inthe entire end to end supply chain to drive actions before excursionsoccur in the production line. Supervised learned models based onhistorical failures and un-supervised models based on limited historicaldata may be used to assess alerts. This system enables machine data totransfer learned knowledge to optimize equipment operation andprescriptively determine maintenance and calibration intervals.

An exemplary alert system may have four active members and onecooperating system, as shown in FIG. 5 . The operator, engineer, ormanufacturing and quality may access the online alert system throughWiFi/BlueTooth, Internet, IR, cellular or Intra-net, or via other knownmeans. Other communication with the system may be through email/Text,for example. Persons having particular administrative levels of access,such as the work cell manager, may access the entire system directly,for example. Alerts to the various group members of a given alert groupmay flow as indicated in FIG. 6 .

FIG. 7 illustrates with particularity a block diagram of the particularaspects of individual layers discussed with respect to FIG. 1 . This isfurther illustrated with respect to the flow diagram of FIG. 8 . Anintegration diagram illustrating integration of the predictive analyticsstack indicated in the examples of FIGS. 7 and 8 is provided in theexamples of FIG. 9 , and is provided for multiple plants in the exampleof FIG. 10 .

FIG. 11 illustrates, by way of example, inputs of historic data toenable analytics that will employ feedback to generate real timedecisions. In the illustration, a first pass yield target is providedagainst which the data input is run through a model to assesscompliance. Thereafter, analytics may indicate modification of certainprocess parameters in order to better generate a yield in accordancewith the target, or to assess likely failures that will contribute tonon-compliance with the target in the future. That is, and as furtherillustrated in FIG. 12 , a feedback loop may be generated using thepredictive analytics, in which data is accumulated and prepared, fedthrough a model, evaluated with respect to the model, deployed via themodel, monitored as to the effectiveness of the model, identified asrelevant to the stated goal of the model, and then modified from adataset standpoint (which obviously indicates modification of machineperformance) to ultimately improve compliance with the model targets.Accordingly, and as illustrated in FIG. 13 , therefrom is generated ahybrid of reactive and predictive models, in which feedback is usedthrough the analytics layer module of the instant embodiments togenerate a predictive model of future performance, which contributes tomodification of current machines from the then-active reactive model.

FIG. 14 illustrates the use of the analytics platform to generate uservisualizations to line operators and quality engineers, as discussedabove with respect to FIG. 5 . In the illustration, factor analysis andtrend analysis is used to predict quality outcomes, and decision logicis employed to generate alerts and modifications to maintain qualitytargets. An exemplary flow of such a model is illustrated in FIG. 15 .An exemplary flow of the decision-making modifications indicated by FIG.15 is illustrated in the example of the flow of FIG. 16 . Of note, FIG.16 may be overlayed with the process flow illustrated in FIG. 5 .

As illustrated in FIG. 16 , upon learning of an event or likely event,an alert may be generated. Such alerts may be delineated by critical andnon-critical alerts, which delineation may be based on likely globaleffect of, for example, line performance. Thereby, part of the analyticsmay be a correlation analysis that identifies the components with thegreatest significance for failures, or with the greatest impact uponfailure, in a given line or facility. The focus of the analytics modelsdiscussed herein may then be placed on these particular machines orcomponents in order to have the greatest impact on uptime and quality,by way of non-limiting example.

Thus may be generated the variable or information value diagram of FIG.17 . More particularly, the discussed analytics may allow for anassessment of variables of importance, wherein the importance may beassessed based on the respective effect of the variables on targetsinput to a performance model. The machine learning model may learn whichvariables are these variables of importance based on both input rulesand actual machine performance data. FIG. 18 is a value treeillustrating the effect that certain value targets are subjected to bycertain variable modifications. These value targets may be interpretedaccording to any number of variables assessed to have predictivecontributions that must be interpreted by the modeling system from rawmachine data. This is illustrated with respect to FIG. 19 .

Of note, the flow of FIG. 19 is indicative of the stack model providedin FIG. 1 , at least in that the raw machine data is accumulated, isconverted to structured data, is subjected as a structured dataset foranalysis by the analytics tool, and is ultimately reported fordecision-making. Moreover, as illustrated in FIG. 19 , the raw machinedata itself may be subjected to analysis in order to make assessments ofoptimized data for entry into the analytics tool. The integration of anexemplary dataset is illustrated with respect to FIG. 20 , in whichrapid analytics are applied to an integrated dataset in order to improvemachine availability.

As mentioned throughout, FIG. 21 illustrates a calculation of theinformation value of particular variables based on the predictivestrength of those variables in an input model. This is furtherillustrated with respect to FIG. 22 .

Needless to say, the accumulation of the foregoing, particularly acrossmultiple lines and/or multiple facilities, contributes to the accrual ofso-called big data. This is further illustrated with respect to FIG. 23. In fact, the size of the dataset leads to difficulties in prior artembodiments. For example, there are a great many available multivariatecombinations, with many opportunities for marginal unit or componentfailure in a mass production system. For example, such a system mayemploy thousands of components, thousands of solder points, millions ofvias, hundreds of parts, and dozens of suppliers, and although each ofthe foregoing parameters may be within tolerance independently, thecombination of parameters may provide an outlier that is undetectable onan independent unit basis. The inability to detect such issues maycontribute to failure and downtime without an assessment of theaggregate dataset, such as through target modeling for an assessment ofnoncompliance with targets. Accordingly, the present embodiments allowfor modeling of such big data, on an individual component and aggregatebasis. This may mean that certain unique analyses are performed that arespecialized for a big data context, such as cluster analysis of data,data distances, multivariable simultaneous analyses, and the like, asindicated in the flow diagram of FIG. 24 .

In an illustrative application of the foregoing, a plastic injectionmolding process may be used to predict real-time production linefailures through machine learning and multi-dimensional data modeling,as disclosed throughout. This may increase yield and decrease scraprate, for example. The analytics may be directed at various high-valueprocess parameters (shot speed, shot pressure, temperature, viscosity,etc) in the efforts to improve yield and reduce non-conformances.

Thermoplastic injection molding is one of the most prevalentmanufacturing processes today. Granular plastic pellets are fed to theinjection molding machine, where they are first softened through heatingzones, then forced under pressure into the mold. The resulting productis cooled and hardened, then ejected from the mold. Injection moldingmachines contain three basic units—a hydraulic pressure system for highpressure mold injection, a plasticizing system to heat and soften theplastic, and the mold itself. The heating cylinder, or barrel,incrementally increases heat as the plastic progresses, and is vented sothat vapor and trapped air can escape. A screw, powered by an electricalmotor and rotating at a specified speed, helps plasticize the pelletsand forces them into the mold. The mold is heated to a quenchtemperature.

FIG. 25 shows the basic layout of an injection molding process underconsideration. Quality and process control is difficult in injectionmolding because of the large number of interacting variables describingthe raw materials, the machinery conditions, the ambient conditions andtemporal aspects.

However, the plastic injection molding production process may sendreal-time process data for an injection molding process, such as interms of the injection velocity and pack pressure. The size and shape ofthe process data window may be determined by certain constrainingboundaries.

FIG. 26 shows boundaries 1, 2 and 5 defining the machine's injectionvelocity and pack pressure limits; 3 defines the combined upper limitsof injection velocity and pack pressure beyond which unacceptable levelsof flash are produced; and 4 determines the lower limits of packpressure and injection velocity beyond which undesirable short shots andsink marks occur. Viable production processes lie within the processwindow determined by these boundaries.

Depending on the part geometry and the primary characteristics ofconcern, different factors or parameters are selected by the analyticsdiscussed herein which would most significantly affect the foregoingconcerns, and these analytics may be based, in part, on actual output,and, in part, on predictive output and targets. For example, somedefects are very speed sensitive, some are very pressure sensitive, andothers are time based or temperature based. When one parameter is movedit affects others, because they are not independent in the injectionmolding process.

Limits may be set around machine-based parameters, for example, but asthe material viscosity changes parameters must be adjusted to maintain astable part. The results of the standard window studies are based on amaterial, and thus do not usually take into consideration the conditionsseen when the viscosity goes up or down—as it often will.

Thus, the analytics discussed herein may allow for a visualization of abalancing of the foregoing and other critical factors to provide realtime data monitoring and feedback to obtain optimal injection moldingperformance. Exemplary critical parameters for plastic injection moldingexamples are provided below, and are illustrated in FIG. 27 :

1. Melt temperature

2. Water temperature

3. First injection transfer—PSI

4. Water temperature

5. Back pressure

6. Cycle time

7. Pack pressure

The site operations screen may report each of the aforementioned processsteps as a visualization, as discussed throughout, such as reportingplasticization, cooling, clamp open-close, build up, and inject high andlow during a cycle, such as during a 14 second cycle by way ofnon-limiting example. The application software may also have securityenabled to prevent data corruption, clean data (no missing values) andcorrelation that may adversely affect this reporting. The visualizationmay also be provided using various language optionality, which may beuser-selectable, such as providing visualization in English, Chinese orSpanish.

FIG. 28 illustrates an exemplary embodiment of a computer processingsystem 400 that may be operably employed in embodiments discussedherein, and that may perform the processing and logic discussedthroughout. That is, the exemplary computing system 400 is just oneexample of a system that may be used in accordance with herein describedsystems and methods. Computing system 400 is capable of executingsoftware, such as an operating system (OS) and one or more computingapplications 490. The software may likewise be suitable for operatingand/or monitoring hardware, such as via inputs/outputs (I/O), using saidapplications 490.

The operation of exemplary computing system 400 is controlled primarilyby computer readable instructions, such as instructions stored in acomputer readable storage medium, such as hard disk drive (HDD) 415,optical disk (not shown) such as a CD or DVD, solid state drive (notshown) such as a USB “thumb drive,” or the like. Such instructions maybe executed within central processing unit (CPU) 410 to cause computingsystem 400 to perform the disclosed operations. In many known computerservers, workstations, PLCs, personal computers, mobile devices, and thelike, CPU 410 is implemented in an integrated circuit called aprocessor.

The various illustrative logics, logical blocks, modules, and engines,described in connection with the embodiments disclosed herein may beimplemented or performed with any of a general purpose CPU, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof, respectively acting as CPU 410.A general-purpose processor may be a microprocessor, but, in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

It is appreciated that, although exemplary computing system 400 is shownto comprise a single CPU 410, such description is merely illustrative,as computing system 400 may comprise a plurality of CPUs 410.Additionally, computing system 100 may exploit the resources of remoteor parallel CPUs (not shown), for example, through local or remotecommunications network 470 or some other data communications means.

In operation, CPU 410 fetches, decodes, and executes instructions from acomputer readable storage medium, such as HDD 415. Such instructions canbe included in the software, such as the operating system (OS),executable programs/applications, and the like. Information, such ascomputer instructions and other computer readable data, is transferredbetween components of computing system 400 via the system's maindata-transfer path. The main data-transfer path may use a system busarchitecture 405, although other computer architectures (not shown) canbe used, such as architectures using serializers and deserializers andcrossbar switches to communicate data between devices over serialcommunication paths.

System bus 405 may include data lines for sending data, address linesfor sending addresses, and control lines for sending interrupts and foroperating the system bus. Some busses provide bus arbitration thatregulates access to the bus by extension cards, controllers, and CPU410. Devices that attach to the busses and arbitrate access to the busare called bus masters. Bus master support also allows multiprocessorconfigurations of the busses to be created by the addition of bus masteradapters containing processors and support chips.

Memory devices coupled to system bus 405 can include random accessmemory (RAM) 425 and read only memory (ROM) 430. Such memories includecircuitry that allows information to be stored and retrieved. ROMs 430generally contain stored data that cannot be modified. Data stored inRAM 425 can generally be read or changed by CPU 410 or othercommunicative hardware devices. Access to RAM 425 and/or ROM 430 may becontrolled by memory controller 420. Memory controller 420 may providean address translation function that translates virtual addresses intophysical addresses as instructions are executed. Memory controller 420may also provide a memory protection function that isolates processeswithin the system and that isolates system processes from userprocesses. Thus, a program running in user mode can normally access onlymemory mapped by its own process virtual address space; it cannot accessmemory within another process' virtual address space unless memorysharing between the processes has been set up.

The steps and/or actions described in connection with the aspectsdisclosed herein may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two, incommunication with memory controller 420 in order to gain the requisiteperformance instructions. That is, the described software modules toperform the functions and provide the directions discussed hereinthroughout may reside in RAM memory, flash memory, ROM memory, EPROMmemory, EEPROM memory, registers, a hard disk, a removable disk, aCD-ROM, or any other form of storage medium known in the art. Any one ormore of these exemplary storage medium may be coupled to the processor410, such that the processor can read information from, and writeinformation to, that storage medium. In the alternative, the storagemedium may be integral to the processor. Further, in some aspects, theprocessor and the storage medium may reside in an ASIC. Additionally, insome aspects, the steps and/or actions may reside as one or anycombination or set of instructions on an external machine readablemedium and/or computer readable medium as may be integrated through I/Oport(s) 485, such as a “flash” drive.

In addition, computing system 400 may contain peripheral controller 435responsible for communicating instructions using a peripheral bus fromCPU 410 to peripherals and other hardware, such as printer 440, keyboard445, and mouse 450. An example of a peripheral bus is the PeripheralComponent Interconnect (PCI) bus.

One or more hardware input/output (I/O) devices 485 may be incommunication with hardware controller 490. This hardware communicationand control may be implemented in a variety of ways and may include oneor more computer busses and/or bridges and/or routers. The I/O devicescontrolled may include any type of port-based hardware (and mayadditionally comprise software, firmware, or the like), such as thedisclosed sensors, gantry arm, loader/unloader, gantry, and otherinputs/outputs, and can also include network adapters and/or massstorage devices from which the computer system 400 can send and receivedata for the purposes disclosed herein. The computer system 400 may thusbe in communication with the Internet or other networked devices/PLCsvia the I/O devices 485 and/or via communications network 470.

Display 460, which is controlled by display controller 455, mayoptionally be used to display visual output generated by computingsystem 400. Display controller 455 may also control, or otherwise becommunicative with, the display. Visual output may include text,graphics, animated graphics, and/or video, for example. Display 460 maybe implemented with a CRT-based video display, an LCD-based display, gasplasma-based display, touch-panel, or the like. Display controller 455includes electronic components required to generate a video signal thatis sent for display.

Further, computing system 400 may contain network adapter 465 which maybe used to couple computing system 400 to an external communicationnetwork 470, which may include or provide access to the Internet, andhence which may provide or include tracking of and access to the processdata discussed herein. Communications network 470 may provide useraccess to computing system 400 with means of communicating andtransferring software and information electronically, and may be coupleddirectly to computing system 400, or indirectly to computing system 400,such as via PSTN or cellular network 480. For example, users maycommunicate with computing system 400 using communication means such asemail, direct data connection, virtual private network (VPN), or otheronline communication services, or the like. Additionally, communicationsnetwork 470 may provide for distributed processing, which involvesseveral computers and the sharing of workloads or cooperative efforts inperforming a task. It is appreciated that the network connections shownare exemplary and other means of establishing communications linksbetween multiple computing systems 400, and/or with remote users, may beused.

It is appreciated that exemplary computing system 400 is merelyillustrative of a computing environment in which the herein describedsystems and methods may operate, and thus does not limit theimplementation of the herein described systems and methods in computingenvironments having differing components and configurations. That is,the inventive concepts described herein may be implemented in variouscomputing environments using various components and configurations.

Those of skill in the art will appreciate that the herein describedapparatuses, engines, devices, systems and methods are susceptible tovarious modifications and alternative constructions. There is nointention to limit the scope of the invention to the specificconstructions described herein. Rather, the herein described systems andmethods are intended to cover all modifications, alternativeconstructions, and equivalents falling within the scope and spirit ofthe disclosure, any appended claims and any equivalents thereto.

What is claimed is:
 1. A predictive analytics engine capable ofproviding real time analytics in a manufacturing system, comprising: adata input to a processing system including at least one processor, thedata input capable of receiving raw data output from at least onemanufacturing machine operable to effect manufacturing in themanufacturing system; the at least one processor being associated with acomputing memory and being suitable for executing non-transitory codefrom the computing memory, the execution of the code causing to occurthe steps of: providing the received raw data to one or more databasesstored in the computing memory which relationally form processed datarelated to the raw data; extracting the processed data from the one ormore databases upon identification of a type of a second manufacturingmachine equivalent to the at least one manufacturing machine from whichwas received the raw data; predictively modelling the extractedprocessed data by applying thereto at least one predictive modelcomprised of operational target data for the second manufacturingmachine to thereby generate feedback related to the type of machine,including at least prospective first time failures for the type ofmachine; modifying performance of the second and the at least onemanufacturing machines based on the feedback from the predictivelymodelling; and displaying to a user of at least a visualization of thefeedback and of the modified performance.
 2. The predictive analyticsengine of claim 1, wherein the feedback comprises critical parameterspredictive of failure by the type of machine to meet the operationaltarget data.
 3. The predictive analytics engine of claim 1, wherein theoperational target data to be met comprises relatively increased yieldcompared to that in the raw data output and reduced scrap compared tothe raw data output.
 4. The predictive analytics engine of claim 1,wherein the modified performance comprises a modifiedbuild-of-materials.
 5. The predictive analytics engine of claim 1,wherein the operational target data comprises a level of lineproductivity.
 6. The predictive analytics engine of claim 1, wherein thepredictively modelling and the feedback is iterative.
 7. The predictiveanalytics engine of claim 1, wherein the feedback comprises a pass orfail compared to the operational target data.
 8. The predictiveanalytics engine of claim 1, wherein the data input resides in a devicelayer.
 9. The predictive analytics engine of claim 8, wherein the devicelayer additionally comprises at least machine-language processing to, inpart, provide the identification of the type of the at least onemanufacturing machine.
 10. The predictive analytics engine of claim 1,wherein the visualization results from a reporting engine suitable togenerate one or more reports.
 11. The predictive analytics engine ofclaim 1, wherein the predictively modelling comprises applying alearning app that learns over repeated applications of the at least onepredictive model.
 12. The predictive analytics engine of claim 11,wherein the predictively modelling iteratively changes over repeatedgenerations of the feedback.
 13. The predictive analytics engine ofclaim 11, wherein the learning app comprises a supervised module. 14.The predictive analytics engine of claim 1, wherein the predictive modelincludes minimized yield loss.
 15. The predictive analytics engine ofclaim 1, wherein the modifying performance includes increasingproduction capacity across multiple ones of the at least onemanufacturing machines substantially simultaneously.
 16. The predictiveanalytics engine of claim 1, wherein the displaying the visualizationcomprises a graphical user interface.
 17. The predictive analyticsengine of claim 16, wherein the graphical user interface is a mobiledevice interface.
 18. The predictive analytics engine of claim 1,wherein the displaying further comprises intercommunicating thevisualization between at least ones of cell managers, suppliers,operators, engineers and operations.
 19. The predictive analytics engineof claim 18, wherein the intercommunicating comprises indicatingproduction line failures.
 20. The predictive analytics engine of claim18, wherein the intercommunicating comprises publishing system alerts.21. The predictive analytics engine of claim 1, wherein the displayingis configurable by at least one authorized user.
 22. The predictiveanalytics engine of claim 1, wherein the predictive model isproduct-centric to a single manufactured product.
 23. The predictiveanalytics engine of claim 1, wherein the relational database furthercomprises an interrelation of operators, manufacturing engineers,operations, quality and component suppliers.
 24. The predictiveanalytics engine of claim 1, wherein the predictive model comprises ashop floor plan.
 25. The predictive analytics engine of claim 1, whereinthe predictively analyzing is at a low data rate during manufacturingoperations.
 26. The predictive analytics engine of claim 1, wherein thepredictive model comprises upstream process inspection and controlparameters.
 27. The predictive analytics engine of claim 26, wherein thepredictive model comprises quality metrics.